code: def forward(self, data):
      x, edge_index, batch = data.x, data.edge_index, data.batch
      edge_weight = None
      x = F.relu(self.conv1(x, edge_index))
      xs = [global_mean_pool(x, batch)]
      for i, conv in enumerate(self.convs):
          x = conv(x=x, edge_index=edge_index, edge_weight=edge_weight)
          x = F.relu(x)
          xs += [global_mean_pool(x, batch)]
          if i % 2 == 0 and i < len(self.convs) - 1:
              pool = self.pools[i // 2]
              x, edge_index, edge_weight, batch, _ = pool(
                  x=x,
                  edge_index=edge_index,
                  edge_weight=edge_weight,
                  batch=batch,
              )
      x = self.jump(xs)
      x = F.relu(self.lin1(x))
      x = F.dropout(x, p=0.5, training=self.training)
      x = self.lin2(x)
      return F.log_softmax(x, dim=-1)
  
file_path: pytorch_geometric/benchmark/kernel/asap.py
imports: import torch
  import torch.nn.functional
  from torch.nn import Linear
  from torch_geometric.nn import ASAPooling, GraphConv, JumpingKnowledge, global_mean_pool
label_desc: Use image node_id 3 for calling the ASAP obj's underlying member method code with example usage: obj.forward(data) and returns: F